FV3-LAM CAM Ensemble Predictions and Consensus Products for Predicting Heavy Rain for the 2023 FFaIR Experiment

High-resolution ensemble forecasts show promise toward improving predictions of high-impact warm season rainfall events, including flash flooding. The US National Weather Service is developing a high-resolution Convection-Allowing Model (CAM) ensemble, the Rapid Refresh Forecasting System (RRFS). To maximize the utility of RRFS for predicting intense rainfall events, it is thus necessary to explore high resolution ensemble consensus forecasts and ensemble consensus products for improving forecasts of heavy rain that might lead to flash flooding and other impacts.

In recent years, the Center for Analysis and Prediction of Storms (CAPS) has designed ensemble forecast products and run real-time forecasts using the FV3-Limited Area Model (FV3-LAM) for the NOAA/WPC Hydrometeorology Testbed (HMT) Flash Flood and Intense Rainfall Experiments (FFaIR). In recent years, CAPS has produced real-time CAM ensembles consisting of 12-16 members covering a CONUS-wide domain on a 3 km grid. The ensembles were created through variation of the physics suites employed and by applying perturbations to the initial-conditions and lateral boundary conditions. Ensemble consensus products have been developed that include an ensemble mean, a probability matched (PM) mean, a localized probability matched (LPM) mean, and a Spatially Aligned Mean (SAM) products. The SAM technique attempts to preserve the structure and local maxima from individual CAM members by locally adjusting precipitation fields to a common location. LPM can be applied to the re-aligned members so SAM products include both SAM and SAM followed by LPM (SAM-LPM).

Beginning in 2022, CAPS produced artificial intelligence machine learning (ML) products to predict rainfall, using a U-Net deep learning approach to forecast the probability of rainfall exceeding 0.5 and 1.0 inches during 6-hour periods. The algorithm used a combination of selected CAPS CAM ensemble members and members of the operational High Resolution Ensemble Forecast (HREF). The CAPS U-Net for rainfall prediction in the 2023 FFaIR was trained on CAPS FV3 and HREF data from the summers of 2020 - 2022.

The consensus methods described in this section were evaluated subjectively during FFaIR in the summer of 2023, and will be objectively scored using the MET-Plus verification tools. One result from 2022 FFaIR is included in the abstract image, Spatial Bias and Equitable Threat Score at the 1-inch threshold for 24 h precipitation. Results for individual ensemble members are shown in the colored bars, plus three different ensemble means in the orange shades, at 3 verification times, 12-36h, 36-60h, 60-84h over 17 cases from 2022 FFaIR. The results show some variation among members, but also illustrate the value of the ensemble means as they score well above any individual member. Details of the ensemble consensus and ML methods and more ensemble results from recent FFaIR experiments will be presented at the conference.